

Databricks and Anaconda Business compete in the data processing and analytics market. Based on feedback, Databricks has an edge due to its scalable cloud environment and collaborative features, while Anaconda Business is distinguished by its package management and security.
Features: Databricks is notable for its advanced machine learning capabilities using Spark clusters, strong automation and integration features, and excellent scalability. Anaconda Business is highlighted for its extensive data science libraries, package management, and seamless integration of Python and R environments.
Room for Improvement: Databricks users suggest enhancing visualization, error messaging, and offering more affordable pricing while broadening tool integrations. Anaconda Business could improve by having a more intuitive interface, faster environment setups, and better collaborative tools.
Ease of Deployment and Customer Service: Databricks is praised for its easy cloud deployment and robust support, despite some concerns about response times. Anaconda Business is valued for smooth installation in on-premises and hybrid environments, with feedback pointing to a need for better documentation and support.
Pricing and ROI: Databricks is seen as expensive but offers a clear ROI through efficiency improvements. Anaconda Business is appreciated for its cost-effectiveness and low overheads, especially in academic settings, though there is room to improve cost-value alignment for enterprise use.
Everyone being able to work smoothly without unnecessary delays.
I have seen a return on investment; specifically, when we talk about efficiency, it's both time-saving and money-saving.
I have seen a return on investment with time saved by 50% and less downtime, allowing the team to deliver projects faster with fewer errors.
This reduction in both time and money resulted in real-time impact and significant cost savings.
For a lot of different tasks, including machine learning, it is a nice solution.
When it comes to big data processing, I prefer Databricks over other solutions.
Anaconda Business customer support is very active with a quick response time.
Overall, support was reliable when we needed it, just not super-fast every single time.
The customer support for Anaconda Business provides a better approach.
Whenever we reach out, they respond promptly.
As of now, we are raising issues and they are providing solutions without any problems.
I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.
As more environments or users get added, it still runs smoothly without major slowdowns.
Anaconda Business scales very well because it is built around centralized environment and package management.
Anaconda does not have scalability restrictions as it depends on the type of machine running it.
The sky's the limit with Databricks.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Databricks is an easily scalable platform.
Earlier, setting up or troubleshooting conflicts could take anywhere from thirty minutes to an hour, but now most setups just work.
Anaconda Business is stable to an extent, but it sometimes crashes on systems with insufficient RAM.
They release patches that sometimes break our code.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
Databricks is definitely a very stable product and reliable.
It would also be nice to have clearer error messages when something fails, so it is easier to understand what went wrong without digging too much.
They should enhance the security point of view; it's good, but it needs some more advanced features.
The pricing should be a little lower for a single person to use, as it might be affordable for an organization, but for my single use, it is difficult.
Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly.
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
Anaconda is an open-source tool, so I do not pay anything for it.
My experience with pricing, setup cost, and licensing is that it is a little costly, but it is useful.
My experience with pricing, setup cost, and licensing indicates that it is a bit costly, but it is useful.
It is not a cheap solution.
I believe that in terms of credits for Databricks, we're spending between £15,000 and £20,000 a month.
Anaconda Business has positively impacted my organization because, when discussing the security point of view, it's exceptional; when comparing it to other solutions, Anaconda Business is superior.
We find the advanced security, governance, and collaborative features for organizations using Python and R particularly useful.
Anaconda Business positively impacts our organization by protecting us from compliance and security risks while keeping the environment consistent, allowing our team to focus on insight and innovation instead of worrying about setups, security, and software issues.
Databricks' capability to process data in parallel enhances data processing speed.
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
| Product | Market Share (%) |
|---|---|
| Databricks | 9.3% |
| Anaconda Business | 2.4% |
| Other | 88.3% |

| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 2 |
| Large Enterprise | 19 |
| Company Size | Count |
|---|---|
| Small Business | 26 |
| Midsize Enterprise | 12 |
| Large Enterprise | 56 |
Anaconda Business provides a comprehensive platform for data science applications, integrating extensive libraries and seamless Python and R compatibility, enhancing developer productivity.
Anaconda Business offers data science professionals a platform combining extensive library support with pre-built models and seamless integration of Python and R environments. With features like a user-friendly interface and integrated Jupyter Notebook, it facilitates real-time code execution and debugging. Environmental management is simplified via Conda, while cloud-based access and package management enhance user experience. Community support and integration with applications like RStudio and Jupyter aid in data science and deep learning tasks.
What are the key features of Anaconda Business?Anaconda Business is widely used in industries like machine learning and data analysis, where it's employed for tasks such as predictive modeling and data visualization. Organizations utilize its compatibility with tools like Scikit-learn and TensorFlow for creating statistical models, supporting applications in fields such as analytics, education, subrogation, and warehouse management.
Databricks offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.
Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.
What features make Databricks unique?
What benefits can users expect from Databricks?
In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.
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